title: A prospective validation of the IOTA logistic regression models (LR1 and LR2) in comparison to subjective pattern recognition for the diagnosis of ovarian cancer.
abstract: OBJECTIVE
  This study aimed to assess the accuracy of the International Ovarian Tumour Analysis (IOTA) logistic regression models (LR1 and LR2) and that of subjective pattern recognition (PR) for the diagnosis of ovarian cancer.
  
  
  METHODS
  This was a prospective single-center study in a general gynecology unit of a tertiary hospital during 33 months. There were 292 consecutive women who underwent surgery after an ultrasound diagnosis of an adnexal tumor. All examinations were by a single level 2 ultrasound operator, according to the IOTA guidelines. The malignancy likelihood was calculated using the IOTA LR1 and LR2. The women were then examined separately by an expert operator using subjective PR. These were compared to operative findings and histology. The sensitivity, specificity, area under the curve (AUC), and accuracy of the 3 methods were calculated and compared.
  
  
  RESULTS
  The AUCs for LR1 and LR2 were 0.94 [95% confidence interval (CI), 0.92-0.97] and 0.93 (95% CI, 0.90-0.96), respectively. Subjective PR gave a positive likelihood ratio (LR+ve) of 13.9 (95% CI, 7.84-24.6) and a LR-ve of 0.049 (95% CI, 0.022-0.107). The corresponding LR+ve and LR-ve for LR1 were 3.33 (95% CI, 2.85-3.55) and 0.03 (95% CI, 0.01-0.10), and for LR2 were 3.58 (95% CI, 2.77-4.63) and 0.052 (95% CI, 0.022-0.123). The accuracy of PR was 0.942 (95% CI, 0.908-0.966), which was significantly higher when compared with 0.829 (95% CI, 0.781-0.870) for LR1 and 0.836 (95% CI, 0.788-0.872) for LR2 (P < 0.001).
  
  
  CONCLUSIONS
  The AUC of the IOTA LR1 and LR2 were similar in nonexpert's hands when compared to the original and validation IOTA studies. The PR method was the more accurate test to diagnose ovarian cancer than either of the IOTA models.
authors.affiliation: *Gynaecological Diagnostic Outpatient Treatment Unit, University College Hospital; †Department of Statistical Science, University College London; and ‡Department of Women's Cancer, University College London, Elizabeth Garrett Anderson, Institute for Women's Health, London, United Kingdom.
authors.forename: Natalie
authors.initials: N
authors.lastname: Nunes
authors.forename: Gareth
authors.initials: G
authors.lastname: Ambler
authors.forename: Wee-Liak
authors.initials: WL
authors.lastname: Hoo
authors.forename: Joel
authors.initials: J
authors.lastname: Naftalin
authors.forename: Xulin
authors.initials: X
authors.lastname: Foo
authors.forename: Martin
authors.initials: M
authors.lastname: Widschwendter
authors.forename: Davor
authors.initials: D
authors.lastname: Jurkovic
mesh_terms: D000328:Adult; D062625:Cystadenofibroma / Q000175:diagnosis / Q000453:epidemiology; D003537:Cystadenoma / Q000175:diagnosis* / Q000453:epidemiology; D003937:Diagnosis, Differential; D005188:False Negative Reactions; D005260:Female; D006801:Humans; D016015:Logistic Models; D008875:Middle Aged; D010051:Ovarian Neoplasms / Q000175:diagnosis* / Q000453:epidemiology; D010363:Pattern Recognition, Automated / Q000592:standards / Q000706:statistics & numerical data*; D017410:Practice Guidelines as Topic; D012680:Sensitivity and Specificity